Performance-Driven Employee Productivity  Analytics for Enhanced Decision-Making and  Efficiency

Duggireddy sai Pranith Reddy; Gone Manish Reddy; Sultani Sirisha; H. Venkateswara Reddy

doi:10.71097/IJSAT.v16.i2.4456

Performance-Driven Employee Productivity Analytics for Enhanced Decision-Making and Efficiency

Author(s)	Duggireddy sai Pranith Reddy, Gone Manish Reddy, Sultani Sirisha, H. Venkateswara Reddy
Country	India
Abstract	The growth of a company requires good and functional employees who contribute to the success of the company’s project and future works. To make this come true the employees working must be aware of their productivity levels so that it can help the companies to suggest roles and responsibilities for the employees based on their work. We collect data from various sources, including attendance records, task completion rates,working hours, and performance reviews. Using statistical methods and machine learning models, we identify key productivity indicators and patterns in employee performance. Measuring the impact of factors like work hours, breaks, experience and team collaboration on performance using correlation analysis is done. Historical data is fed into machine leaning algorithms like regression models and classification techniques in order to predict productivity levels. Other clustering techniques categorize employees by work pattern and then allow managers to give personal support to them. The Natural Language Processing (NLP) is used to perform the sentiment analysis of the employee feedback and understand the effect workplace satisfaction has on productivity. The findings from this project aid organizations with making data driven decisions around data to better tune their work environments, workloads and the efficiency of their employees. Policies that can be put into action by the data science firms can ensure that the productivity of the employee is maximized to ensure the growth of the company.
Keywords	Employee Productivity , Performance Analysis, Machine Learning, Statistical Methods, Productivity Indicators
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-02
Cite This	Performance-Driven Employee Productivity Analytics for Enhanced Decision-Making and Efficiency - Duggireddy sai Pranith Reddy, Gone Manish Reddy, Sultani Sirisha, H. Venkateswara Reddy - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.4456
DOI	https://doi.org/10.71097/IJSAT.v16.i2.4456
Short DOI	https://doi.org/g9hbr5

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Performance-Driven Employee Productivity Analytics for Enhanced Decision-Making and Efficiency

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